Effective and Efficient: Large-Scale Dynamic City Express

Zhang, Siyuan; Qin, Lu; Zheng, Yu; Cheng, Hong

doi:10.1109/tkde.2016.2604806

Cited by 17 publications

(4 citation statements)

References 33 publications

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“…As for the more common express systems, researchers also focus on developing an effective and efficient intelligent express system by optimizing the order matching problem. Zhang et al first systematically study the large-scale dynamic city express problem, and adopt a batch assignment strategy that computes the pickup-delivery routes for a group of requests received in a short period rather than dealing with each request individually [87]. Rather than using the heuristic-based methods, Li et al further proposed a soft-label clustering algorithm named BDSB to dispatch parcels to couriers in each region [59].…”

Section: Drl For Order Matching In Logistic Systemsmentioning

confidence: 99%

Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey

Zong¹,

Feng²,

Tong³

et al. 2021

Preprint

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Recent technology development brings the booming of numerous new Demand-Driven Services (DDS) into urban lives, including ridesharing, on-demand delivery, express systems and warehousing. In DDS, a service loop is an elemental structure, including its service worker, the service providers and corresponding service targets. The service workers should transport either humans or parcels from the providers to the target locations. Various planning tasks within DDS can thus be classified into two individual stages: 1) Dispatching, which is to form service loops from demand/supply distributions, and 2)Routing, which is to decide specific serving orders within the constructed loops. Generating high-quality strategies in both stages is important to develop DDS but faces several challenging. Meanwhile, deep reinforcement learning (DRL) has been developed rapidly in recent years. It is a powerful tool to solve these problems since DRL can learn a parametric model without relying on too many problem-based assumptions and optimize long-term effect by learning sequential decisions. In this survey, we first define DDS, then highlight common applications and important decision/control problems within. For each problem, we comprehensively introduce the existing DRL solutions, and further summarize them in https://github.com/tsinghua-fib-lab/DDS Survey. We also introduce open simulation environments for development and evaluation of DDS applications. Finally, we analyze remaining challenges and discuss further research opportunities in DRL solutions for DDS.

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Section: Drl For Order Matching In Logistic Systemsmentioning

confidence: 99%

Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey

Zong¹,

Feng²,

Tong³

et al. 2021

Preprint

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show abstract

“…delivering parcels to customers and picking up packages from customers. Zhang et al [32] proposed some heuristic algorithms to guide couriers to deliver and pick up packages at the same time in the system. Li et al [15] proposed a multi-agent reinforcement learning based model to manage the couriers to complete these two types of tasks.…”

Section: Related Workmentioning

confidence: 99%

“…After some working time, c 1 returned to transit station s 1 to unload the packages on his or her van. Couriers in the system are required to depart from and return to transit stations by specific time, to fit the schedule of trucks which pick up packages from stations regularly [32] [15]. We name this specific time interval as an episode, e.g.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Multi-Agent Reinforcement Learning in Express System

Zheng

Yang

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Express systems are widely deployed in many major cities. One type of important tasks in the system is to pick up packages from customers in time. As pickup requests come in real time and there are many couriers picking up packages, how to dispatch couriers to ensure the cooperation among them and to complete more pickup tasks in a long time, is very important but challenging. In this paper, we propose a reinforcement learning based framework to learn courier dispatching policies. At first, we divide the city into independent regions, inner each of which a constant number of couriers pick up packages at the same time. Besides reducing problem complexity, city division has practical operation benefits. Afterwards, we focus on each region separately. For each region, we propose a Cooperative Multi-Agent Reinforcement Learning model, i.e. CMARL, to learn the optimal courier dispatching policy in it. CMARL tries to maximize the total number of completed pickup tasks by all couriers in a long time. Our model achieves this target by combining two Markov Decision Processes, one to guarantee the cooperation among couriers, and the other one to ensure the longterm optimization. After obtaining the value functions of these two MDPs, a new value function is designed to trade off them, based on which we can infer the courier dispatching policy. Experiments based on real-world road network data and historical express data from Beijing are conducted, to confirm the superiority of our model compared with nine baselines. CCS CONCEPTS • Information systems → Spatial-temporal systems; • Theory of computation → Reinforcement learning.

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“…Information technology and e-commerce make the express delivery industry burst out a great vitality [1][2][3]. According to the Global Express Association (GEA) report in 2015, the international express delivery industry contributed 0.19 percentage points to Global GDP, considering only the members of GEA [4].…”

Section: Introductionmentioning

confidence: 99%

A Scheduling Strategy of Mobile Parcel Lockers for the Last Mile Delivery Problem

Wang

Chen

2020

PROMET

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In the form of unattended Collection-and-Delivery Points (CDP), the fixed parcel lockers can save courier miles and improve the delivery efficiency. However, due to the fixed location and combination, the fixed parcel locker cannot accommodate the change of demands effectively. In this paper, an approach to supplementing fixed lockers by mobile parcel lockers to meet the demands of the last mile delivery has been proposed. With the goal of minimizing the operating cost, the location and route optimization problems of mobile parcel lockers are integrated into a non-linear integer programming model. An embedded GA has been developed to optimally determine the locations of distribution points, the number of mobile parcel lockers needed by each distribution point and the schedules and routes of mobile parcel lockers, simultaneously. Finally, a numerical example is given to compare the optimization results of the schemes with and without the aggregation problem. The results show that the scheme with the aggregation problem can greatly save the delivery time. However, for the scheme without the aggregation problem, time windows are more continuous, so it saves the number of vehicles.

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Effective and Efficient: Large-Scale Dynamic City Express

Cited by 17 publications

References 33 publications

Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey

Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey

Cooperative Multi-Agent Reinforcement Learning in Express System

A Scheduling Strategy of Mobile Parcel Lockers for the Last Mile Delivery Problem

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